Automatic selection of representative media clips

ABSTRACT

A system determines human recognizable labels for portions of an electronic media stream, gathers data associated with the electronic media stream from a number of media players, and determines at least one section of the electronic media stream with a particular media feature. The system selects a representative clip for the electronic media stream based on information regarding the labeled portions, the gathered data, and the at least one section.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No. 14/017,184, filed Sep. 3, 2013, which is a continuation of U.S. patent application Ser. No. 12/889,191, filed Sep. 23, 2010, which is a continuation of U.S. patent application Ser. No. 11/289,433, filed on Nov. 30, 2005, the entire contents of all are hereby incorporated by reference in their entirety.

BACKGROUND Field of the Invention

Implementations described herein relate generally to processing of electronic media and, more particularly, to the identification of a representative clip for the electronic media.

Description of Related Art

Currently, online music stores associated with providers, such as iTunes, Real Player, Windows Media Player, Amazon, Yahoo, and Napster, offer a representative clip of songs for free. The representative clip is typically about thirty seconds in length. There currently exists two methods by which the representative clip is selected.

In the first method, human operators select a representative clip from each song. The human operators identify start points for the representative clip. Given that there are multiple languages and thousands of songs released weekly, the first method scales poorly.

In the second method, rules are established for selecting the representative clip. The rules typically identify the first thirty seconds of the song. Slightly more complicated rules might start the representative clip at the first audible sound, so that silence is not included. This often means that songs with long intros or big differences between the chorus and other parts of the songs may be less appealing to potential purchasers because the representative clip does not contain the memorable portion of the songs.

SUMMARY

According to one aspect, a method may include collecting information associated with user interaction with regard to an electronic media stream and a number of media players; classifying the information as positive user actions, negative user actions, and neutral user actions with regard to the electronic media stream; and identifying a representative clip for the electronic media stream based on the classified information.

According to another aspect, a system may include a clip selector to determine human recognizable labels for portions of an electronic media stream, collect data associated with user interaction with regard to the electronic media stream and a number of media players, and select a representative clip for the electronic media stream based on information regarding the labeled portions and the collected data.

According to yet another aspect, a system may include a clip selector to collect data regarding user interaction with regard to a number of media players and the electronic media stream, identify at least one section of the electronic media stream based on a feature associated with the at least one section, and determine a representative clip for the electronic media stream based on information regarding the collected data and the at least one section.

According to a further aspect, a method may include determining human recognizable labels for portions of an electronic media stream; determining at least one section of the electronic media stream with a particular feature; and selecting a representative clip for the electronic media stream based on information regarding the labeled portions and the at least one section.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, explain the invention. In the drawings,

FIG. 1 illustrates a concept consistent with the principles of the invention;

FIG. 2 is a diagram of an exemplary system in which systems and methods consistent with the principles of the invention may be implemented;

FIG. 3 is an exemplary diagram of a device that may be used to implement the clip selector of FIG. 2;

FIG. 4 is an exemplary functional diagram of the clip selector of FIG. 2;

FIG. 5 is an exemplary functional diagram of the audio deconstructor of FIG. 4;

FIG. 6 is an exemplary functional diagram of the audio player information gatherer of FIG. 4;

FIG. 7 is an exemplary functional diagram of the audio analyzer of FIG. 4;

FIG. 8 is an exemplary functional diagram of the representative clip selector of FIG. 4;

FIG. 9 is a flowchart of exemplary processing for selecting a representative clip according to an implementation consistent with the principles of the invention; and

FIGS. 10-14 are diagrams of an exemplary implementation consistent with the principles of the invention.

DETAILED DESCRIPTION

The following detailed description of the invention refers to the accompanying drawings. The same reference numbers in different drawings may identify the same or similar elements. Also, the following detailed description does not limit the invention.

As used herein, “electronic media” may refer to different forms of audio and video information, such as radio, sound recordings, television, video recording, and streaming Internet content. The description to follow will describe electronic media in terms of audio information, such as an audio stream or file. It should be understood that the description may equally apply to other forms of electronic media, such as video streams or files.

Overview

FIG. 1 illustrates a concept consistent with the principles of the invention. As shown in FIG. 1, an audio player, such as iTunes, permits individuals to purchase music. A potential purchaser may initiate the audio player and proceed to the “music store” by selecting the appropriate item (e.g., link). The link may cause a web site associated with the provider of the audio player to be accessed.

Within the music store, the potential purchaser can browse and search for music. Typically, the music store may provide information regarding the music that it offers. The music store may categorize its music by genre, popularity, and/or release date, and permit access to its music by keyword searching.

As shown in FIG. 1, assume that the potential purchaser desires music associated with the Commodores. The potential purchaser can access information regarding collections (e.g., albums) of the Commodores by keyword searching or perusal of the music categories. The music store may permit the potential purchaser to listen to one or more (or all) of the songs in a collection. For example, the music store may present the potential purchaser with a list of songs associated with the collection and permit the potential purchaser to select one or more of the songs.

When the potential purchaser selects a song, the audio player may play a representative clip from the song. Implementations consistent with the principles of the invention may automatically determine which portion of the song best represents the song and, therefore, would be most likely to “sell” the song. As described in detail below, information regarding labeled portions of the song, human interaction with an audio player, and/or audio features associated with the song may help identify the audio clip (e.g., a thirty second clip) of the song that best represents the song.

Automatically selecting an audio clip that best represents a song has many uses and advantages. It may scale well to select audio clips for thousands of songs, possibly in different languages, that are typically released in any given week. The automatic clip selection may be useful for online music stores, such as those provided by Yahoo, Apple, Microsoft, Napster, Real, Amazon, and others, that permit customers to listen to a portion of a song before purchasing the song. The automatic clip selection might also be useful for traditional “brick and mortar” stores, such as Barnes and Nobel, Best Buy, Borders, Starbucks, and others, that often have listening stations to permit customers to listen to a portion of a song before purchasing the song. The automatic clip selection might further be useful for online sellers of tickets, such as Tickmaster, that may permit customers to listen to a portion of one or more songs that may be featured at a concert before purchasing a ticket to the concert. The automatic clip selection could also be useful at venues, such as concert arenas, where attendees might be able to listen to a portion of songs associated with performers.

Exemplary System

FIG. 2 is an exemplary diagram of a system 200 in which systems and methods consistent with the principles of the invention may be implemented. As shown in FIG. 2, system 200 may include clip selector 210. In one implementation, clip selector 210 is implemented as one or more devices that may each include any type of computing device capable of receiving an audio stream (from a network or file) and select a clip of the audio stream that is representative of the audio stream.

FIG. 3 is an exemplary diagram of a device 300 that may be used to implement clip selector 210. Device 300 may include a bus 310, a processor 320, a main memory 330, a read only memory (ROM) 340, a storage device 350, an input device 360, an output device 370, and a communication interface 380. Bus 310 may include a path that permits communication among the elements of device 300.

Processor 320 may include a processor, microprocessor, or processing logic that may interpret and execute instructions. Main memory 330 may include a random access memory (RAM) or another type of dynamic storage device that may store information and instructions for execution by processor 320. ROM 340 may include a ROM device or another type of static storage device that may store static information and instructions for use by processor 320. Storage device 350 may include a magnetic and/or optical recording medium and its corresponding drive.

Input device 360 may include a mechanism that permits an operator to input information to device 300, such as a keyboard, a mouse, a pen, voice recognition and/or biometric mechanisms, etc. Output device 370 may include a mechanism that outputs information to the operator, including a display, a printer, a speaker, etc. Communication interface 380 may include any transceiver-like mechanism that enables device 300 to communicate with other devices and/or systems.

As will be described in detail below, clip selector 210, consistent with the principles of the invention, may perform certain audio processing-related operations. Clip selector 210 may perform these operations in response to processor 320 executing software instructions contained in a computer-readable medium, such as memory 330. A computer-readable medium may be defined as a physical or logical memory device and/or carrier wave.

The software instructions may be read into memory 330 from another computer-readable medium, such as data storage device 350, or from another device via communication interface 380. The software instructions contained in memory 330 may cause processor 320 to perform processes that will be described later. Alternatively, hardwired circuitry may be used in place of or in combination with software instructions to implement processes consistent with the principles of the invention. Thus, implementations consistent with the principles of the invention are not limited to any specific combination of hardware circuitry and software.

FIG. 4 is an exemplary functional diagram of clip selector 210. Clip selector 210 may include audio deconstructor 410, audio player information gatherer 420, audio analyzer 430, and representative clip selector 440. Audio deconstructor 410 may receive an audio stream, deconstruct the audio stream into portions, and identify human recognizable labels (e.g., intro, verse, chorus, bridge, etc.) for the portions. These portions may have particular recognizable characteristics and can easily be recognized by humans. One of the benefits of determining the labeled portions of the audio stream is that the portions can assist in the selection of a representative clip. For example, the chorus of the audio stream might be the most recognizable portion of the audio stream. Therefore, it might be beneficial to include at least one instance of the chorus in the representative clip.

Audio player information gatherer 420 may gather information regarding human interaction with an audio player in connection with the audio stream. For example, audio player information gatherer 420 may aggregate information regarding user actions for a number of users and audio players. The user actions might include turning up or down the volume during the playing of an audio stream, fast forwarding over one or more parts of an audio stream, repeating the playing of one or more parts of an audio stream, and pausing the playing of an audio stream. One of the benefits of aggregating information regarding user actions is that the information can assist in the selection of a representative clip. For example, parts of the audio stream that have invoked positive actions (defined below) from users might constitute favorable part(s) of the audio stream. Therefore, it might be beneficial to include at least part of one or more of these favorable parts in the representative clip.

Audio analyzer 430 may receive the audio stream and analyze audio features associated with the audio stream. Audio analyzer 430 may analyze audio features associated with one or more frequency ranges of the audio stream to identify information that can assist in the selection of a representative clip. For example, for an audio stream that includes singing, the sections of the audio stream that include singing might be best representative of the audio stream. Therefore, it might be beneficial to include at least part of these sections in the representative clip.

Representative clip selector 440 may receive the outputs of audio deconstructor 410, audio player information gatherer 420, and audio analyzer 430 and determine, based on these outputs, which part of the audio stream would be best representative of the audio stream. Representative clip selector 440 may identify the representative clip by, for example, identifying a time code that corresponds to the start and/or end of the clip. The time code may be stored as metadata associated with the audio stream.

Audio Deconstructor

FIG. 5 is an exemplary functional diagram of audio deconstructor 410. Audio deconstructor 410 is described in copending U.S. patent application Ser. No. 11/289,527 entitled “DECONSTRUCTING AUDIO STREAM INTO HUMAN RECOGNIZABLE PORTIONS,” filed concurrently herewith, and which is incorporated herein by reference.

As shown in FIG. 5, audio deconstructor 410 may include portion identifier 510 and label identifier 520. Portion identifier 510 may receive an audio stream, such as a music file or stream, and deconstruct the audio stream into audio portions (e.g., audio portion 1, audio portion 2, audio portion 3, . . . , audio portion N (where N≥2)). In one implementation, portion identifier 510 may be based on a model that uses a machine learning, statistical, or probabilistic technique to predict break points between the portions in the audio stream, which is described in more detail below. The input to the model may include the audio stream and the output of the model may include break point identifiers (e.g., time codes) relating to the beginning and end of each portion of the audio stream.

Label identifier 520 may receive the break point identifiers from portion identifier 510 and determine a label for each of the portions. In one implementation, label identifier 510 may be based on a model that uses a machine learning, statistical, or probabilistic technique to predict a label for each of the portions of the audio stream, which is described in more detail below. The input to the model may include the audio stream with its break point identifiers (which identify the portions of the audio stream) and the output of the model may include the identified portions of the audio stream with their associated labels.

Portion identifier 510 and/or label identifier 520 may be based on models. The model for portion identifier 510 may be referred to as the “portion model” and the model for label identifier 520 may be referred to as the “label model.” While the portion model and the label model will be described as separate models that are trained differently, it may be possible for a single model to be trained to perform the functions of both models.

Portion Model

The training set for the portion model might include human training data and/or audio data. Human operators who are well versed in music might identify the break points between portions of a number of audio streams. For example, human operators might listen to a number of music files or streams and identify the break points among the intro, verse, chorus, bridge, and/or outro. The audio data might include a number of audio streams for which human training data is provided.

Human training data and attributes associated with the audio data may be analyzed to form a set of rules for identifying break points between portions of other audio streams. The rules may be used to form the portion model.

The audio data attributes might include volume, intensity, patterns, and/or other characteristics of the audio stream that might signify a break point. For example, a change in volume within an audio stream might be used as an indicator of a break point.

Additionally, or alternatively, a change in level (intensity) for one or more frequency ranges might be used as an indicator of a break point. An audio stream may include multiple frequency ranges associated with, for example, the human vocal frequency range and one or more frequency ranges associated with the instrumental frequencies (e.g., a bass frequency, a treble frequency, and/or one or more mid-range frequencies). Changes in a single frequency range or correlated changes in multiple frequency ranges may be used as an indicator of a break point.

Additionally, or alternatively, a change in pattern (e.g., beat pattern) might be used as an indicator of a break point. For example, a window around each instance (e.g., time point) in the audio stream (e.g., ten seconds prior to and ten second after the instance) may be analyzed to compare the beats per second in each frequency range within the window. A change in the beats per second within one or more of the frequency ranges might indicate a break point. In one implementation, changes in the beats per second might be correlated for all frequency ranges and used as an indicator of a break point.

Rules might be generated for the portion model based on one or more of the audio data attributes, such as those identified above. Any of several well known techniques may be used to generate the model, such as logic regression, boosted decision trees, random forests, support vector machines, perceptrons, and winnow learners. The portion model may determine the probability that an instance in an audio stream is the beginning (or end) of a portion based on one or more audio data attributes associated with the audio stream:

-   -   P(portion|audio attribute(s)),         where “audio attribute(s)” might refer to one or more of the         audio data attributes identified above.

The portion model may generate a “score,” which may include a probability output and/or an output value, for each instance in the audio stream that reflects the probability that the instance is a break point. The highest scores (or scores above a threshold) may be determined to be actual break points in the audio stream. Break point identifiers (e.g., time codes) may be stored for each of the instances that are determined to be break points. Pairs of identifiers (e.g., a time code and the subsequent or preceding time code) may signify the different portions in the audio stream.

The output of the portion model may include break point identifiers (e.g., time codes) relating to the beginning and end of each portion of the audio stream.

Label Model

The training set for the label model might include human training data, audio data, and/or audio feature information. Human operators who are well versed in music might label the different portions of a number of audio streams. For example, human operators might listen to a number of music files or streams and label their different portions, such as the intros, the verses, the choruses, the bridges, and/or the outros. The human operators might also identify genres (e.g., rock, jazz, classical, etc.) with which the audio streams are associated. The audio data might include a number of audio streams for which human training data is provided along with break point identifiers (e.g., time codes) relating to the beginning and end of each portion of the audio streams. Attributes associated with an audio stream may be used to identify different portions of the audio stream. Attributes might include frequency information and/or other characteristics of the audio stream that might indicate a particular portion. Different frequencies (or frequency ranges) may be weighted differently to assist in separating those one or more frequencies that provide useful information (e.g., a vocal frequency) over those one or more frequencies that do not provide useful information (e.g., a constantly repeating bass frequency) for a particular portion or audio stream.

The audio feature information might include additional information that may assist in labeling the portions. For example, the audio feature information might include information regarding common portion labels (e.g., intro, verse, chorus, bridge, and/or outro). Additionally, or alternatively, the audio feature information might include information regarding common formats of audio streams (e.g., AABA format, verse-chorus format, etc.). Additionally, or alternatively, the audio feature information might include information regarding common genres of audio streams (e.g., rock, jazz, classical, etc.). The format and genre information, when available, might suggest a signature (e.g., arrangement of the different portions) for the audio streams. A common signature for audio streams belonging to the rock genre, for example, may include the chorus appearing once, followed by the bridge, and then followed by the chorus twice consecutively.

Attributes associated with the audio streams, the portions identified by the break points, the audio feature information, and the human training data may be analyzed to form a set of rules for labeling portions of other audio streams. The rules may be used to form the label model.

Some of the rules that may be generated for the label model might include:

-   -   Intro: An intro portion may start at the beginning of the         audible frequencies.     -   Verse: A verse portion generally includes sound within the vocal         frequency range. There may be multiple verses with the same or         similar chord progression but slightly different lyrics. Thus,         similar waveform shapes in the instrumental frequencies with         different waveform shapes in the vocal frequencies may be         verses.     -   Bridge: A bridge portion commonly occurs within an audio stream         other than at the beginning or end. Generally, a bridge is         different in both chord progression and lyrics from the verses         and chorus.     -   Chorus: A chorus portion generally includes a portion that         repeats (in both chord progression and lyrics) within the audio         stream and may be differentiated from the verse in that the         lyrics are generally the same between different occurrences of         the chorus.     -   Outro: An outro portion may include the last portion of an audio         stream and generally trails off of the last chorus.

The label model may be formed using any of several well known techniques, such as logic regression, boosted decision trees, random forests, support vector machines, perceptrons, and winnow learners. The label model may determine the probability that a particular label is associated with a portion in an audio stream based on one or more attributes, audio feature information, and/or information regarding other portions associated with the audio stream:

-   -   P(label|portion, audio attribute(s), audio feature information,         other portions),         where “portion” may refer to the portion of the audio stream for         which a label is being determined, “audio attribute(s)” may         refer to one or more of the audio stream attributes identified         above that are associated with the portion, “audio feature         information” may refer to one or more types of audio feature         information identified above, and “other portions” may refer to         information (e.g., characteristics, labels, etc.) associated         with other portions in the audio stream.

The label model may generate a “score,” which may include a probability output and/or an output value, for a label that reflects the probability that the label is associated with a particular portion. The highest scores (or scores above a threshold) may be determined to be actual labels for the portions of the audio stream.

The output of the label model may include information regarding the portions (e.g., break point identifiers) and their associated labels. This information may be provided to representative clip selector 440 (FIG. 4).

Audio Player Information Gatherer

FIG. 6 is an exemplary functional diagram of audio player information gatherer 420. As shown in FIG. 6, audio player information gatherer 420 may collect information regarding human interaction with an audio player, such as audio player 610, in connection with an audio stream.

Audio player 610 may include a play/pause button 611, stop button 612, rewind button 613, fast forward button 614, progress slider 615, mute button 616, and volume button 617. Play/pause button 611 may permit a user to play an audio stream and/or pause the playing of the audio stream. Rewind button 613 may permit the user to rewind the playing of the audio stream. Fast forward button 614 may permit the user to fast forward during playing of the audio stream. Slider 615 may provide a visual indicator of the progress of the playing of the audio stream. Slider 615 may also permit the user to fast forward and rewind during playing of the audio stream. Mute button 616 may permit the user to mute the audio during playing of the audio stream. Volume button 617 may permit the user to increase and/or decrease the volume of the audio during playing of the audio stream.

Information regarding user actions with regard to audio player 610 may be provided to audio player information gatherer 420. The user actions might include information regarding any action taken by the user during playing of an audio stream. For example, the user actions might include pausing playing of the audio stream, stopping playing of the audio stream, rewinding or fast forwarding during playing of the audio stream, muting the audio during playing of the audio stream, and/or increasing or decreasing the volume of the audio during playing of the audio stream. The information provided to audio player information gatherer 420 might identify a user action, when the user action occurred (e.g., a time code within the audio stream at which the user action occurred), and the audio stream (e.g., something that uniquely identifies the audio stream being played by the user).

While a single audio player 610 is shown in FIG. 6, audio player information gatherer 420 may collect information from a number of audio players from the same audio player provider or different audio player providers. For example, audio player information gatherer 420 may be affiliated with one or more audio player providers and the audio players of the one or more audio player providers (or the one or more audio player providers themselves) may send information to audio player information gatherer 420 (or some other component or memory associated with audio player information gatherer 420).

Alternatively, software may be installed on a client device to monitor user interaction with one or more audio players. The software may take the form of executable code, such as a plug-in, an applet, a dynamic link library (DLL), or a similar type of executable object or process. The software may identify user actions associated with an audio player and provide information to audio player information gatherer 420 (or some other component or memory associated with audio player information gatherer 420).

Audio player information gatherer 420 may classify each of the user actions as positive, negative, or neutral, which may reflect how the user felt about what the user was hearing. A positive action might include turning up the volume during playing of the audio stream or replaying a part of the audio stream, which may reflect that the user likes a certain part of the audio stream. A negative action might include fast forwarding, hovering over the fast forward button, or turning down the volume during playing of the audio stream, which may reflect that the user dislikes a certain part of the audio stream. A neutral action might include pausing the playing of the audio stream or muting the audio associated with the audio stream, which may reflect nothing about the user's feelings with regard to the audio stream.

Audio player information gatherer 420 may combine the user action information from the audio players in some manner and output information regarding positive, negative, and/or neutral actions as audio player metadata to representative clip selector 440.

Audio Analyzer

FIG. 7 is an exemplary functional diagram of audio analyzer 430. As shown in FIG. 7, audio analyzer 430 may receive an audio stream and divide the audio stream into a number of frequency ranges. The particular number of frequency ranges that the audio stream is divided into is unimportant, though at least one of the frequency ranges may correspond to the human vocal range or a frequency range corresponding to one or more musical instruments. Alternatively, audio analyzer 430 may isolate one or more frequency ranges, where at least one of the frequency ranges may correspond to the human vocal range or a frequency range corresponding to one or more musical instruments.

Audio analyzer 430 may analyze audio features, such as levels (intensity), associated with one or more of the frequency ranges. In one implementation, audio analyzer 430 may identify certain sections of the audio stream that have particular audio features. For example, audio analyzer 430 may determine the sections of the audio stream that include particular levels (e.g., levels above a threshold) in the human vocal range (for an audio stream that includes singing). Audio analyzer 430 may use time codes to identify the sections of the audio stream that have particular audio features.

Audio analyzer 430 may output information regarding the sections that have particular audio features, such as time codes associated with the sections, to representative clip selector 440.

Representative Clip Selector

FIG. 8 is an exemplary functional diagram of representative clip selector 440. As shown in FIG. 8, representative clip selector 440 may receive information regarding labeled portions from audio deconstructor 410, audio player metadata from audio player information gatherer 420, and information regarding sections with particular audio features from audio analyzer 430. Representative clip selector 440 may use this information as inputs to a function that determines a representative clip for an audio stream.

In one implementation, representative clip selector 440 may score the information that it obtains from audio deconstructor 410, audio player information gatherer 420, and audio analyzer 430. With respect to the information regarding labeled portions, representative clip selector 440 may score certain portions identified by audio deconstructor 410 higher than other portions of the audio stream. For example, representative clip selector 440 may score the chorus portion higher than other portions of the audio stream because the chorus portion typically contains the most memorable part of an audio stream. In a simple scoring scheme, representative clip selector 440 may assign a score of one to the chorus portions and a score of zero or some other value between zero and one to the other portions (e.g., intro, outro, verses, bridge, etc.).

With respect to the audio player metadata, representative clip selector 440 may score the parts of the audio stream that have positive associated audio player metadata higher than the parts of the audio stream that have negative or neutral associated audio player metadata because the parts with positive associated audio player metadata may be inferred to be favorable parts of the audio stream because they invoked positive actions. In a simple scoring scheme, representative clip selector 440 may assign a score of one to the parts that have positive associated audio player metadata and a score of zero or some other value between zero and one to the other parts (e.g., the parts that have negative or neutral associated audio player metadata).

With respect to the information regarding the sections with particular audio features, representative clip selector 440 may score the sections of the audio stream that have particular audio features higher than other sections of the audio stream because the sections that have particular audio features might better represent the audio stream. In a simple scoring scheme, representative clip selector 440 may assign a score of one to the sections that have particular audio features and a score of zero or some other value between zero and one to the other sections of the audio stream.

Representative clip selector 440 may input these scores into a function that may combine the scores in some manner. The output of the function might identify a piece of the audio stream to include in the representative clip. For example, the function may combine the scores and identify the piece of the audio stream that obtains the highest total score. If the identified piece is greater than the size of the representative clip (e.g., thirty seconds), then a subset of the identified piece may be used. If the identified piece is smaller than the size of the representative clip, then the representative clip may be selected to begin with, end with, or otherwise contain the identified piece.

The description above described simple scoring schemes and a simple function that combined the scores to identify the representative clip. In other implementations, more complex scoring schemes and/or functions may be used to determine the representative clip for the audio stream.

Exemplary Processing

FIG. 9 is a flowchart of exemplary processing for selecting a representative clip according to an implementation consistent with the principles of the invention. Processing may begin with the inputting of an audio stream into clip selector 210 (block 910). The audio stream might correspond to a music file or stream and may be one of many audio streams to be processed by clip selector 210. The inputting of the audio stream may correspond to selection of a next audio stream from a set of stored audio streams for processing by clip selector 210.

The audio stream may be processed to identify portions of the audio stream and labels for the identified portions (block 920). In one implementation, the audio stream may be input into a portion model that is trained to identify the different portions of the audio stream with high probability. For example, the portion model may identify the break points between the different portions of the audio stream based on the attributes associated with the audio stream. The break points may identify where the different portions start and end. Human recognizable labels may be identified for each of the identified portions. In one implementation, the audio stream, information regarding the break points, and possibly audio feature information (e.g., genre, format, etc.) may be input into a label model that is trained to identify labels for the different portions of the audio stream with high probability. For example, the label model may analyze the instrumental and vocal frequencies associated with the different portions and relationships between the different portions to determine the appropriate labels.

Audio player metadata may be collected (block 930). For example, information regarding user interaction with one or more types of audio players may be gathered. Data particular to the audio stream may be classified as positive, negative, and/or neutral based on the particular actions taken by the user(s).

The audio stream may also be analyzed to determine sections of the audio stream that have particular audio features (block 940). For example, audio features may be analyzed with regard to one or more frequency ranges of the audio stream. Those sections of the audio stream that have audio features that match or exceed some criteria in one or more of the frequency ranges may be identified.

A representative clip may be identified based on the labeled portions, the audio player metadata, and/or the sections with particular audio features (block 950). For example, information regarding the labeled portions, the audio player metadata, and the sections with particular audio features may be input to a function that identifies a piece of the audio stream that is best representative of the audio stream.

Example

FIGS. 10-14 are diagrams of an exemplary implementation consistent with the principles of the invention. As shown in FIG. 10, assume that the clip selector receives the song “O Susanna.” The clip selector may identify a representative clip for the song.

The clip selector may identify break points between portions of the song based on attributes associated with the song and identify labels associated with the portions. As shown in FIG. 11, assume that the clip selector identifies break points with high probability at time codes 0:18, 0:38, 0:58, 1:18, 1:38, and 1:58, and determines that a first portion that occurs between 0:00 and 0:18 is verse 1, a second portion that occurs between 0:18 and 0:38 is the chorus, a third portion that occurs between 0:38 and 0:58 is verse 2, a fourth portion that occurs between 0:58 and 1:18 is the chorus, a fifth portion that occurs between 1:18 and 1:38 is verse 3, and a sixth portion that occurs after 1:38 until the end of the song at 1:58 is the chorus.

As shown in FIG. 12, the clip selector may gather information (i.e., audio player metadata) concerning positive user actions (and possibly negative and neutral user actions) with regard to the playing of the song on one or more audio players. The clip selector may obtain the information directly from the one or more audio players. Alternatively, the clip selector may obtain the information from an audio player provider that may accumulate such information.

As shown in FIG. 13, the clip selector may analyze audio features associated with the song to identify sections of the song that have particular audio features in one or more frequency ranges. For example, the song may be divided into frequency ranges or one or more frequency ranges may be isolated within the song. Assume that the clip selector isolates a frequency range corresponding to the human vocal range, as shown in FIG. 13. The clip selector may then determine those sections of the song that have particular audio features in the human vocal range. For example, the clip selector may identify sections that have singing, which may be reflected in the waveforms associated with the human vocal range.

As shown in FIG. 14, the clip selector may assign scores to the different data generated/collected above. For the labeled portions, the clip selector may assign a score of one to the chorus portions and a score of zero to the other portions. For the audio player metadata, the clip selector may assign a score of one to parts of the song that are associated with positive user actions and a score of zero to the other parts. For the sections with particular audio features, the clip selector may assign a score of one to the sections identified as having the particular audio features and a score of zero to the other sections. The clip selector may then combine the scores in some manner to identify a representative clip.

As shown in FIG. 14, the clip selector identified a piece of the song that obtained a highest combined score for the representative clip. While FIG. 14 shows a single piece of the song as obtaining the highest score, it may be possible for different pieces of the audio stream to obtain the same highest score. In this case, the clip selector may simply pick one of them for the representative clip.

CONCLUSION

Implementations consistent with the principles of the invention may automatically determine which piece of electronic media may best represent the electronic media and, therefore, would be most likely to “sell” the electronic media.

The foregoing description of preferred embodiments of the invention provides illustration and description, but is not intended to be exhaustive or to limit the invention to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of the invention.

For example, while a series of acts has been described with regard to FIG. 9, the order of the acts may be modified in other implementations consistent with the principles of the invention. Further, non-dependent acts may be performed in parallel.

While the preceding description focused on identifying a representative clip for an audio stream, the description may equally apply to the identification of representative clips for other forms of electronic media, such as video streams. For example, representative clips could be selected from a movie by choosing the genre and selecting a genre appropriate clip. For an action movie, for example, a representative clip might be selected based on the percentage of pixels that change from the last frame (e.g., that may signal motion and action), the tempo of the background music, and/or one or more portions identified by a model of a deconstructor. In this case, the model may be trained based on human training data by which human operators may select scenes that are the funniest, most drama-filled, most action-filled, etc. The model may learn what factors are shared by those scenes to identify similar scenes in other movies. For a drama movie, a representative clip might be selected based on background music that includes minor chords (e.g., that may be representative of sad music) and/or one or more portions identified by the model of the deconstructor. For a comedy movie, a representative clip might be selected based on background music that includes major chords (e.g., that may be representative of happy music) and/or one or more portions identified by the model of the deconstructor.

Also, it has been described that a representative clip is determined based on information regarding labeled portions, electronic media player metadata, and information regarding sections with particular electronic media features. In other implementations, a representative clip may be determined based on information regarding labeled portions alone, electronic media player metadata alone, or information regarding sections with particular electronic media features alone, or any combination thereof.

Further, terms, such as “portion,” “part,” “section,” and “piece,” have been used to identify a subset of an electronic media stream. It should be understood that any of these terms may refer to any subset of the electronic media stream, which is less than the entire electronic media stream.

Moreover, the term “stream” has been used in the description above. The term is intended to mean any form of data whether embodied in a carrier wave or stored as a file in memory.

It will be apparent to one of ordinary skill in the art that aspects of the invention, as described above, may be implemented in many different forms of software, firmware, and hardware in the implementations illustrated in the figures. The actual software code or specialized control hardware used to implement aspects consistent with the principles of the invention is not limiting of the invention. Thus, the operation and behavior of the aspects were described without reference to the specific software code—it being understood that one of ordinary skill in the art would be able to design software and control hardware to implement the aspects based on the description herein.

No element, act, or instruction used in the present application should be construed as critical or essential to the invention unless explicitly described as such. Also, as used herein, the article “a” is intended to include one or more items. Where only one item is intended, the term “one” or similar language is used. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise. 

What is claimed is:
 1. A method, comprising: determining labels associated with portions of an audio stream; determining at least one audio feature associated with at least one section of the audio stream, wherein a respective one of the portions of the audio stream overlaps in time with a respective one of the at least one section of the audio stream; generating scores for the determined labels associated with the portions and at least one score for the at least one audio feature associated with the at least one section of the audio stream; and identifying, by a computing device, a representative clip for the audio stream in view of a combination of the scores for determined labels associated with the portions and the at least one score for the at least one section.
 2. The method of claim 1, wherein determining the labels for the portions of the audio stream comprises: inputting the audio stream into a machine learning model; identifying the portions of the audio stream in view of the machine learning model, wherein the machine learning model has been trained to identify the portions of the audio stream in view of attributes of the audio stream; and determining the labels for the identified portions.
 3. The method of claim 1, further comprising: gathering data of user interaction with a plurality of audio players, wherein the user interaction occurs during playback of the audio stream using the plurality of audio players, wherein the user interaction is associated with parts of the audio stream.
 4. The method of claim 3, wherein gathering the data of the user interaction with the plurality of audio players comprises: collecting a user action identifier of an action by a user with an audio player of the plurality of audio players, the action being substantially contemporaneous with the playback of the audio stream on the audio player; and collecting a time code associated with the parts of the audio stream at which the action occurred.
 5. The method of claim 4, wherein gathering the data of the user interaction with the plurality of audio players further comprises: classifying the user interaction as at least one of positive actions, negative actions, or neutral actions.
 6. The method of claim 5, wherein the positive actions comprise turning up a volume concurrent with the playback of the audio stream.
 7. The method of claim 5, wherein the negative actions comprise at least one of fast forwarding the audio stream or turning down a volume concurrent with the playback of the of the audio stream.
 8. The method of claim 5, wherein the neutral actions comprise at least one of pausing the playback of the audio stream or muting audio concurrent with the playback of the audio stream.
 9. The method of claim 3, wherein gathering the data of the user interaction with the plurality of audio players comprises: obtaining the data from an audio player provider associated with the plurality of audio players.
 10. The method of claim 3, wherein the plurality of audio players are associated with a single audio player provider.
 11. The method of claim 3, wherein the plurality of audio players are associated with a plurality of unaffiliated audio player providers.
 12. The method of claim 1, wherein determining the at least one audio feature associated with the at least one section of the audio stream comprises: identifying one or more frequency ranges associated with the audio stream; analyzing waveforms associated with the one or more frequency ranges; and identifying the at least one section of the audio stream in which a particular audio feature of the at least one audio feature exists in view of the analyzed waveforms.
 13. The method of claim 1, wherein identifying the representative clip for the audio stream comprises: inputting the scores for the determined labels and the at least one score for the at least one section into a function; and outputting an indicator identifying a piece of the audio stream as the representative clip.
 14. A system, comprising: a memory; and a processing device, coupled to the memory, to: determine labels associated with portions of an audio stream; determine at least one audio feature associated with at least one section of the audio stream, wherein a respective one of the portions of the audio stream overlaps in time with a respective one of the at least one section of the audio stream; generate scores for the determined labels associated with the portions and at least one score for the at least one audio feature associated with the at least one section of the audio stream; and identify a representative clip for the audio stream in view of a combination of the scores for determined labels associated with the portions and the at least one score for the at least one section.
 15. The system of claim 14, wherein to determine the labels for the portions of the audio stream, the processing device further to: input the audio stream into a machine learning model; identify the portions of the audio stream in view of the machine learning model, wherein the machine learning model has been trained to identify the portions of the audio stream in view of attributes of the audio stream; and determine the labels for the identified portions.
 16. The system of claim 14, the processing device further to: gather data of user interaction with a plurality of audio players, wherein the user interaction occurs during playback of the audio stream using the plurality of audio players, wherein the user interaction is associated with parts of the audio stream.
 17. The system of claim 14, wherein to determine the at least one audio feature associated with the at least one section of the audio stream, the processing device further to: identify one or more frequency ranges associated with the audio stream; analyze waveforms associated with the one or more frequency ranges; and identify the at least one section of the audio stream in which a particular audio feature of the at least one audio feature exists in view of the analyzed waveforms.
 18. A non-transitory computer-readable medium to store instructions that, when executed by a processing device, cause the processing device to perform operations comprising: determining labels associated with portions of an audio stream; determining at least one audio feature associated with at least one section of the audio stream, wherein a respective one of the portions of the audio stream overlaps in time with a respective one of the at least one section of the audio stream; generating scores for the determined labels associated with the portions and at least one score for the at least one audio feature associated with the at least one section of the audio stream; and identifying, by the processing device, a representative clip for the audio stream in view of a combination of the scores for determined labels associated with the portions and the at least one score for the at least one section.
 19. The non-transitory computer-readable medium of claim 18, the operations further comprising: gathering data of user interaction with a plurality of audio players, wherein the user interaction occurs during playback of the audio stream using the plurality of audio players, wherein the user interaction is associated with parts of the audio stream.
 20. The non-transitory computer-readable medium of claim 18, wherein determining the labels for the portions of the audio stream further comprises: inputting the audio stream into a machine learning model; identifying the portions of the audio stream in view of the machine learning model, wherein the machine learning model has been trained to identify the portions of the audio stream in view of attributes of the audio stream; and determining the labels for the identified portions. 